Why Darwin would have loved evolutionary game theory

The origin and progression of cancer is widely viewed as “somatic evolution” driven by the accumulation of random genetic changes. This theoretical model, however, neglects fundamental conditions for evolution by natural selection, which include competition for survival and a local environmental context. Recent observations that the mutational burden in different cancers can vary by 2 orders of magnitude and that multiple mutations, some of which are “oncogenic,” are observed in normal tissue suggests these neglected Darwinian dynamics may play a critical role in modifying the evolutionary consequences of molecular events. Here we discuss evolutionary principles in normal tissue focusing on the dynamical tension between different evolutionary levels of selection. Normal somatic cells within metazoans do not ordinarily evolve because their survival and proliferation is governed by tissue signals and internal controls (e.g. telomere shortening) that maintain homeostatic function. The fitness of each cell is, thus, identical to the whole organism, which is the evolutionary level of selection. For a cell to evolve, it must acquire a self-defined fitness function so that its survival and proliferation is determined entirely by its own heritable phenotypic properties. Cells can develop independence from normal tissue control through randomly accumulating mutations that disrupt its ability to recognize or respond to all host signals. A self-defined fitness function can also be gained non-genetically when tissue control signals are lost due to injury, inflammation, or infection. Accumulating mutations in cells without a self-defined fitness function will produce no evolution - consistent with reports showing mutations, including some that would ordinarily be oncogenic, are present in cells from normal tissue. Furthermore, once evolution begins, Darwinian forces will promote mutations that increase fitness and eliminate those that do not. Thus, cancer cells will typically have a mutational burden similar to adjacent normal cells and many (perhaps most) mutations observed in cancer cells occurred prior to somatic evolution and may not contribute to the cell’s malignant phenotype.

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“…Evolution by natural selection requires three conditions [16, 17]: 1. A population of individuals with heritable phenotypic variation.…”

Section: Darwinian Dynamics a Reviewmentioning

confidence: 99%

Mutations, evolution and the central role of a self-defined fitness function in the initiation and progression of cancer

Gatenby

Brown

2017

Biochimica et Biophysica Acta (BBA) - Reviews on Cancer

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“…Incidentally, this also applies to evolutionary game theory (Hofbauer and Sigmund, 1998;Maynard Smith, 1982) and its extensions (Brown, 2016), which are generally also based on fitness functions, e.g. in the form of payoff matrices.…”

Section: Fitness and Optimalitymentioning

confidence: 99%

Towards a mechanistic foundation of evolutionary theory

Doebeli

Ispolatov

Simon

2017

eLife

108

119

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Most evolutionary thinking is based on the notion of fitness and related ideas such as fitness landscapes and evolutionary optima. Nevertheless, it is often unclear what fitness actually is, and its meaning often depends on the context. Here we argue that fitness should not be a basal ingredient in verbal or mathematical descriptions of evolution. Instead, we propose that evolutionary birth-death processes, in which individuals give birth and die at ever-changing rates, should be the basis of evolutionary theory, because such processes capture the fundamental events that generate evolutionary dynamics. In evolutionary birth-death processes, fitness is at best a derived quantity, and owing to the potential complexity of such processes, there is no guarantee that there is a simple scalar, such as fitness, that would describe long-term evolutionary outcomes. We discuss how evolutionary birth-death processes can provide useful perspectives on a number of central issues in evolution.

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“…We use Evolutionary Game Theory (EGT) to investigate how individual task preferences develop in the lifetime of a colony, and how specialisation can emerge as a result of these choices [57,7]. It is important to note that we do not make any reference to evolutionary processes.…”

Section: Introductionmentioning

confidence: 99%

“…We use Evolutionary Game Theory (EGT) [43,7] to investigate how individual task preferences develop in the lifetime of a colony, and how specialisation emerges as a result of these choices. While EGT was originally conceived to model the evolution of behaviour, it has also been interpreted as "learning game theory" to explore behavioural change in ecological timescales [34,60].…”

Section: Introductionmentioning

confidence: 99%

A computational model of task allocation in social insects – ecology and interactions alone can drive specialisation

Chen

Meyer

García

2018

Preprint

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Social insect colonies are capable of allocating their workforce in a decentralised fashion; addressing a variety of tasks and responding effectively to changes in the environment. This process is fundamental to their ecological success, but the mechanisms behind it remain poorly understood. While most models focus on internal and individual factors, empirical evidence highlights the importance of ecology and social interactions. To address this gap we propose a game theoretical model of task allocation. Individuals are characterised by a trait that determines how they split their energy between the two prototypical tasks of foraging and regulation. A viable colony needs to learn to adequately split their energy between the two tasks. We study two different social processes: individuals can learn to play this game by relying exclusively on their own experience, or by relying on the experiences of others via social learning. A computational model allows for direct manipulation of individual interactions and ecology. We find that social organisation can be completely determined by the ecology alone: irrespective of interaction details, weakly specialised colonies in which all individuals tend to both tasks emerge when foraging is cheap; harsher environments, on the other hand, lead to strongly specialised colonies in which each individual fully engages in a single tasks. We compare the outcomes of self-organised task allocation with optimal group performance. Counter to intuition, strongly specialised colonies perform suboptimally, whereas the group performance of weakly specialised colonies is closer to optimal. Differences in social interactions are salient when the colony deals with dynamic environments. Colonies whose individuals rely on their own experience are better able to deal with change. Our computational model is aligned with 1 All rights reserved. No reuse allowed without permission.(which was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.The copyright holder for this preprint . http://dx.doi.org/10.1101/315846 doi: bioRxiv preprint first posted online May. 7, 2018; mathematical predictions in tractable limits. We believe this different kind of model is useful in framing relevant and important empirical questions.

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Why Darwin would have loved evolutionary game theory

Cited by 60 publications

References 105 publications

Mutations, evolution and the central role of a self-defined fitness function in the initiation and progression of cancer

Mutations, evolution and the central role of a self-defined fitness function in the initiation and progression of cancer

Towards a mechanistic foundation of evolutionary theory

A computational model of task allocation in social insects – ecology and interactions alone can drive specialisation

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